Abstract:
The vibration signal monitoring that is being generated by a rotor supported by a rolling
element bearing is becoming important to define reliability of rotary machine. It is most prudent
and useful method for bearing fault detection. Recently, there has been a lot of research on rolling
element bearings fault. The kurtosis is most vital parameter to find inner race fault and outer race
fault. It is enhanced by a proper selection of variable frame sizes and decompositions levels using
wavelet based multi-scale principal component analysis (WMSPCA). It is observed that the
kurtosis changes significantly as compared to the actual kurtosis of the un-decomposed faulty
signals by proper selection of frame size and decompositions level.